Modeling Population Growth in R with the biogrowth Package

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy

Extreme Heat Resistance of Food Borne Pathogens Campylobacter jejuni, Escherichia coli, and Salmonella typhimurium on Chicken Breast Fillet during Cooking

The aim of this research was to determine the decimal reduction times of bacteria present on chicken fillet in boiling water. The experiments were conducted with Campylobacter jejuni, Salmonella, and Escherichia coli. Whole chicken breast fillets were inoculated with the pathogens, stored overnight (4°C), and subsequently cooked. The surface temperature reached 70°C within 30 sec and 85°C within one minute. Extremely high decimal reduction times of 1.90, 1.97, and 2.20 min were obtained for C. jejuni, E. coli, and S. typhimurium, respectively. Chicken meat and refrigerated storage before cooking enlarged the heat resistance of the food borne pathogens. Additionally, a high challenge temperature or fast heating rate contributed to the level of heat resistance. The data were used to assess the probability of illness (campylobacteriosis) due to consumption of chicken fillet as a function of cooking time. The data revealed that cooking time may be far more critical than previously assumed

Relevance of microbial finished product testing in food safety management

Management of microbiological food safety is largely based on good design of processes, products and procedures. Finished product testing may be considered as a control measure at the end of the production process. However, testing gives only very limited information on the safety status of a food. If a hazardous organism is found it means something, but absence in a limited number of samples is no guarantee of safety of a whole production batch. Finished product testing is often too little and too late. Therefore most attention should be focussed on management and control of the hazards in a more pro-active way by implementing an effective food safety management system. For verification activities in a food safety management system, finished product testing may however be useful. For three cases studies; canned food, chocolate and cooked ham, the relevance of testing both of finished products and the production environment is discussed. Since the level of control of different processes can be largely different it is beneficial if the frequency of sampling of finished products and production environments would be related to the associated human health risk, which can be assessed on the basis of risk assessment and epidemiological data. Keywords: Sampling; Verification; Microbiological food safet

A single point mutation in the Listeria monocytogenes ribosomal gene rpsU enables SigB activation independently of the stressosome and the anti-sigma factor antagonist RsbV

Microbial population heterogeneity leads to different stress responses and growth behavior of individual cells in a population. Previously, a point mutation in the rpsU gene (rpsUG50C) encoding ribosomal protein S21 was identified in a Listeria monocytogenes LO28 variant, which leads to increased multi-stress resistance and a reduced maximum specific growth rate. However, the underlying mechanisms of these phenotypic changes remain unknown. In L. monocytogenes, the alternative sigma factor SigB regulates the general stress response, with its activation controlled by a series of Rsb proteins, including RsbR1 and anti-sigma factor RsbW and its antagonist RsbV. We combined a phenotype and proteomics approach to investigate the acid and heat stress resistance, growth rate, and SigB activation of L. monocytogenes EGDe wild type and the ΔsigB, ΔrsbV, and ΔrsbR1 mutant strains. While the introduction of rpsUG50C in the ΔsigB mutant did not induce a SigB-mediated increase in robustness, the presence of rpsUG50C in the ΔrsbV and the ΔrsbR1 mutants led to SigB activation and concomitant increased robustness, indicating an alternative signaling pathway for the SigB activation in rpsUG50C mutants. Interestingly, all these rpsUG50C mutants exhibited reduced maximum specific growth rates, independent of SigB activation, possibly attributed to compromised ribosomal functioning. In summary, the increased stress resistance in the L. monocytogenes EGDe rpsUG50C mutant results from SigB activation through an unknown mechanism distinct from the classical stressosome and RsbV/RsbW partner switching model. Moreover, the reduced maximum specific growth rate of the EGDe rpsUG50C mutant is likely unrelated to SigB activation and potentially linked to impaired ribosomal function

Non-essential genes form the hubs of genome scale protein function and environmental gene expression networks in Salmonella enterica serovar Typhimurium

Background Salmonella Typhimurium is an important pathogen of human and animals. It shows a broad growth range and survives in harsh conditions. The aim of this study was to analyze transcriptional responses to a number of growth and stress conditions as well as the relationship of metabolic pathways and/or cell functions at the genome-scale-level by network analysis, and further to explore whether highly connected genes (hubs) in these networks were essential for growth, stress adaptation and virulence. Results De novo generated as well as published transcriptional data for 425 selected genes under a number of growth and stress conditions were used to construct a bipartite network connecting culture conditions and significantly regulated genes (transcriptional network). Also, a genome scale network was constructed for strain LT2. The latter connected genes with metabolic pathways and cellular functions. Both networks were shown to belong to the family of scale-free networks characterized by the presence of highly connected nodes or hubs which are genes whose transcription is regulated when responding to many of the assayed culture conditions or genes encoding products involved in a high number of metabolic pathways and cell functions. The five genes with most connections in the transcriptional network (wraB, ygaU, uspA, cbpA and osmC) and in the genome scale network (ychN, siiF (STM4262), yajD, ybeB and dcoC) were selected for mutations, however mutagenesis of ygaU and ybeB proved unsuccessful. No difference between mutants and the wild type strain was observed during growth at unfavorable temperatures, pH values, NaCl concentrations and in the presence of H2O2. Eight mutants were evaluated for virulence in C57/BL6 mice and none differed from the wild type strain. Notably, however, deviations of phenotypes with respect to the wild type were observed when combinations of these genes were deleted. Conclusion Network analysis revealed the presence of hubs in both transcriptional and functional networks of S. Typhimurium. Hubs theoretically confer higher resistance to random mutation but a greater susceptibility to directed attacks, however, we found that genes that formed hubs were dispensable for growth, stress adaptation and virulence, suggesting that evolution favors non-essential genes as main connectors in cellular networks

Intraspecific variability in heat resistance of fungal conidia

Microbial species are inherently variable, which is reflected in intraspecies genotypic and phenotypic differences. Strain-to-strain variation gives rise to variability in stress resistance and plays a crucial role in food safety and food quality. Here, strain variability in heat resistance of asexual spores (conidia) of the fungal species Aspergillus niger, Penicillium roqueforti and Paecilomyces variotii was quantified and compared to bacterial variability found in the literature. After heat treatment, a 5.4- to 8.6-fold difference in inactivation rate was found between individual strains within each species, while the strain variability of the three fungal species was not statistically different. We evaluated whether the degree of intraspecies variability is uniform, not only within the fungal kingdom, but also amongst different bacterial species. Comparison with three spore-forming bacteria and two non-spore-forming bacteria revealed that the variability of the different species was indeed in the same order of magnitude, which hints to a microbial signature of variation that exceeds kingdom boundaries.Microbial Biotechnolog

Short- and Long-Term Biomarkers for Bacterial Robustness: A Framework for Quantifying Correlations between Cellular Indicators and Adaptive Behavior

The ability of microorganisms to adapt to changing environments challenges the prediction of their history-dependent behavior. Cellular biomarkers that are quantitatively correlated to stress adaptive behavior will facilitate our ability to predict the impact of these adaptive traits. Here, we present a framework for identifying cellular biomarkers for mild stress induced enhanced microbial robustness towards lethal stresses. Several candidate-biomarkers were selected by comparing the genome-wide transcriptome profiles of our model-organism Bacillus cereus upon exposure to four mild stress conditions (mild heat, acid, salt and oxidative stress). These candidate-biomarkers—a transcriptional regulator (activating general stress responses), enzymes (removing reactive oxygen species), and chaperones and proteases (maintaining protein quality)—were quantitatively determined at transcript, protein and/or activity level upon exposure to mild heat, acid, salt and oxidative stress for various time intervals. Both unstressed and mild stress treated cells were also exposed to lethal stress conditions (severe heat, acid and oxidative stress) to quantify the robustness advantage provided by mild stress pretreatment. To evaluate whether the candidate-biomarkers could predict the robustness enhancement towards lethal stress elicited by mild stress pretreatment, the biomarker responses upon mild stress treatment were correlated to mild stress induced robustness towards lethal stress. Both short- and long-term biomarkers could be identified of which their induction levels were correlated to mild stress induced enhanced robustness towards lethal heat, acid and/or oxidative stress, respectively, and are therefore predictive cellular indicators for mild stress induced enhanced robustness. The identified biomarkers are among the most consistently induced cellular components in stress responses and ubiquitous in biology, supporting extrapolation to other microorganisms than B. cereus. Our quantitative, systematic approach provides a framework to search for these biomarkers and to evaluate their predictive quality in order to select promising biomarkers that can serve to early detect and predict adaptive traits

Microbial testing in food safety : Effect of specificity and sensitivity on sampling plans - how does the OC curve move

Obviously the quality of test methods does have an impact on the performance of sampling plans. A low sensitivity means that actual positive samples are tested negative (i.e. false negatives) and moves the Operating Characteristic (OC) curve to the right. Values of sensitivity above 0.70 do not show large effects on the position of the OC curve. A low specificity means that actual negative samples are tested positive (i.e. false positives) and moves the OC curve to the left. The effect of the specificity of the testing method has a very large effect on the performance of a sampling plan, especially for sampling plans with higher numbers of samples. In those cases specificity should be much larger than 0.99 to have a reasonable performance